Neural network forecasting of time series

In the work, we built a predictive neural network to successfully predict several main classes of radar data, as well as economic indicators. It is a two-layer neural network feedforward network based on the backpropagation error algorithm. The results of forecasting real radio signals. Based on the results of the forecast, it turned out that the neural network ensures the accuracy of the short-term forecast. In this article, we describe the procedures for selecting characteristics for learning a neural network, justifying the choice of the structure of the neural network, training and the results obtained. Time series forecasting is currently an important topic, as it has a wide range of applications (radar, medicine, socio-economic sphere, energy, risk management, engineering applications, etc.). Analysis of works in the field of long-term forecasting of non-deterministic signals showed that at the moment the least studied is the neural network long-term forecasting. The use of neural networks for long-term forecasting is based on their ability to approximate nonlinear functions, the accumulation of history and its application in forecasting and learning ability. The work was based on the method of neural network forecasting using a two-layer network with direct distribution. The implemented neural network can be used to predict real signals of different frequency bands. This study can be very useful in medicine, geodesy, Economics and other areas.